Abstract
This paper studies a risk minimization approach to estimate a transformation model from noisy observations. It is argued that transformation models are a natural candidate to study ranking models and ordinal regression in a context of machine learning. We do implement a structural risk minimization strategy based on a Lipschitz smoothness condition of the transformation model. Then, it is shown how the estimate can be obtained efficiently by solving a convex quadratic program with O(n) linear constraints and unknowns, with n the number of data points. A set of experiments do support these findings.
KP is a postdoctoral researcher with FWO Flanders (A 4/5 SB 18605). S. Van Huffel is a full professor and J.A.K. Suykens is a professor at the Katholieke Universiteit Leuven, Belgium. This research is supported by GOA-AMBioRICS, CoE EF/05/006, FWO G.0407.02 and G.0302.07, IWT, IUAP P6/04, eTUMOUR (FP6-2002-LIFESCIHEALTH 503094).
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Van Belle, V., Pelckmans, K., Suykens, J.A.K., Van Huffel, S. (2009). MINLIP: Efficient Learning of Transformation Models. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_7
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DOI: https://doi.org/10.1007/978-3-642-04274-4_7
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